Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/147120
Title: Portable food-freshness prediction platform based on colorimetric barcode combinatorics and deep convolutional neural networks
Authors: Guo, Lingling
Wang, Ting
Wu, Zhonghua
Wang, Jianwu
Wang, Ming
Cui, Zequn
Ji, Shaobo
Cai, Jianfei
Xu, Chuanlai
Chen, Xiaodong
Keywords: Science::Chemistry::Analytical chemistry
Issue Date: 2020
Source: Guo, L., Wang, T., Wu, Z., Wang, J., Wang, M., Cui, Z., Ji, S., Cai, J., Xu, C. & Chen, X. (2020). Portable food-freshness prediction platform based on colorimetric barcode combinatorics and deep convolutional neural networks. Advanced Materials, 32(45), 2004805-.
Journal: Advanced Materials 
Abstract: Artificial scent screening systems (known as electronic noses, E-noses) have been researched extensively. A portable, automatic, and accurate, real-time E-nose requires both robust cross-reactive sensing and fingerprint pattern recognition. Few E-noses have been commercialized because they suffer from either sensing or pattern-recognition issues. Here, cross-reactive colorimetric barcode combinatorics and deep convolutional neural networks (DCNNs) are combined to form a system for monitoring meat freshness that concurrently provides scent fingerprint and fingerprint recognition. The barcodes—comprising 20 different types of porous nanocomposites of chitosan, dye, and cellulose acetate—form scent fingerprints that are identifiable by DCNN. A fully supervised DCNN trained using 3475 labeled barcode images predicts meat freshness with an overall accuracy of 98.5%. Incorporating DCNN into a smartphone application forms a simple platform for rapid barcode scanning and identification of food freshness in real time. The system is fast, accurate, and non-destructive, enabling consumers and all stakeholders in the food supply chain to monitor food freshness.
URI: https://hdl.handle.net/10356/147120
ISSN: 1521-4095
Rights: This is the peer reviewed version of the following article: Guo, L., Wang, T., Wu, Z., Wang, J., Wang, M., Cui, Z., Ji, S., Cai, J., Xu, C. & Chen, X. (2020). Portable food-freshness prediction platform based on colorimetric barcode combinatorics and deep convolutional neural networks. Advanced Materials, 32(45), 2004805-., which has been published in final form at https://doi.org/10.1002/adma.202004805. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MSE Journal Articles

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